code: def inference_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      debug: Optional[bool] = False,
  ):
      """Runs a single step of training."""
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
      capture_intermediates = lambda mdl, _: mdl.name == "pre_logits"
      logits, intermediate = flax_model.apply(
          variables,
          batch["inputs"],
          train=False,
          mutable=False,
          debug=debug,
          capture_intermediates=capture_intermediates,
      )
      return (
          nn.softmax(logits, axis=-1),
          intermediate["intermediates"]["pre_logits"]["__call__"][0],
      )
  
imports: import datetime
  import functools
  import os
  import pickle
  from typing import Any, Callable, Optional, Type
  from absl import logging
  from clu import metric_writers
  from flax import jax_utils
  import flax.linen
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.tasseo import classification_trainer
  from scenic.projects.tasseo import train_utils
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: inference_step(train_state, batch) and returns: nn,  intermediate